AI & Học máy
Học máy, sinh tăng cường truy xuất (RAG), ứng dụng AI và thu thập dữ liệu web.
Detailed Analysis of grouping_COSMO_Margules_all.ipynb
This Jupyter notebook implements a Physics-Informed Neural Network (PINN) prediction pipeline for Vapor-Liquid Equilibrium (VLE) data using COSMO-based molecular descriptors combined with the Margules activity coefficien
Đọc bài viết →chemprop_VP
Note: These environments include a CPU-only version of PyTorch. If GPU acceleration is desired, please refer to the PyTorch homepage for the correct version and installation command. The recommended PyTorch version is 1.
Đọc bài viết →AI for Chemistry - 問題分析與改良方案
問題描述： 多處使用硬編碼的絕對路徑，嚴重影響可移植性。
Đọc bài viết →Software Requirements Specification (SRS)
Project: Pediatric Q&A via LINE (Python Flask + LINE + RAG with OpenAI)
Đọc bài viết →Comprehensive Machine Learning & Deep Learning Curriculum
A complete set of professional, textbook-quality teaching PDFs covering a depth-first path from mathematical foundations to production deep learning — written for someone who already codes well but wants real understandi
Đọc bài viết →How to use this curriculum
This is a depth-first path written for someone who already programs well. The assumption is Python comfort and solid software-engineering instincts; the gap we are closing is the conceptual and mathematical one. Every ph
Đọc bài viết →Bayesian Inference — Preface: how to use this book
There is one equation behind this entire book, and it fits on a single line
Đọc bài viết →Distributions, Conjugate Pairs, and Formulas
A compact reference to the book's key results. @p4.8cmp9.2cm@ Quantity & Formula Bayes' theorem & P( D)=P(D ) P( )P(D) Working form & p( D) p(D ) p( ) Evidence (marginal likelihood) & p(D)= p(D ) p( ) d Posterior predict
Đọc bài viết →What Is Bayesian Thinking?
Before any formula, Bayesian inference is a way of reasoning: hold beliefs as probabilities, and revise them in proportion to the evidence. This chapter sets up that worldview — probability as degree of belief, the contr
Đọc bài viết →Bayes' Theorem
Now we make the update rule exact. Bayes' theorem is a short consequence of the definition of conditional probability, but its implications are deep enough to have reshaped statistics, science, and AI. This chapter deriv
Đọc bài viết →The Prior, the Likelihood, and the Posterior
In Chapters – the hypotheses were discrete (sick or healthy). The real power of Bayesian inference appears when the unknown is a continuous parameter — a probability, a rate, a mean — so the prior and posterior become di
Đọc bài viết →Conjugate Priors and Updating
Computing the posterior means doing the integral p(D)= p(D )p( ) d — usually hard. Conjugate priors are a beautiful special case where the integral is free: prior and posterior belong to the same family, so updating is j
Đọc bài viết →Choosing Priors
The prior is what makes Bayesian inference both powerful and controversial: it lets you inject knowledge, but it also forces you to be explicit about your assumptions. This chapter is a practical guide to choosing priors
Đọc bài viết →Summarizing the Posterior and Bayesian Decisions
The posterior is a full distribution, but people need answers: a single estimate, an interval, a yes/no decision. This chapter shows how to summarize a posterior responsibly (point estimates and credible intervals) and h
Đọc bài viết →Naive Bayes Classifiers
Naive Bayes is Bayes' theorem turned into a fast, surprisingly effective classifier. Despite an assumption that is almost always false — that features are independent given the class — it powers spam filters, document cl
Đọc bài viết →Bayesian versus Frequentist Inference
The two great schools of statistics answer the same questions in fundamentally different ways. Understanding the contrast sharpens your grasp of both — and clears up the most persistent confusions in applied statistics,
Đọc bài viết →Bayesian Networks and Graphical Models
Real problems have many interacting variables. Bayesian networks (directed graphical models) make such joint distributions tractable by drawing the dependencies as a graph: an arrow means “directly influences,” a missing
Đọc bài viết →Computational Bayes: MCMC and Variational Inference
Conjugacy is a luxury; most real posteriors have no closed form because the evidence integral p(D)= p(D )p( ) d is intractable. The modern Bayesian toolkit sidesteps it two ways: MCMC draws samples from the posterior wit
Đọc bài viết →Bayesian Regression and Hierarchical Models
Now we put the machinery to work on the models practitioners use every day. Bayesian regression replaces point-estimate coefficients with full posteriors, delivering principled uncertainty on every prediction. Hierarchic
Đọc bài viết →Model Comparison, Evidence, and Occam's Razor
Inference within a model assumes the model is right. But which model should we use? Bayesian model comparison answers this with the very quantity we worked so hard to avoid — the evidence p(D) — which turns out to automa
Đọc bài viết →Bayes in Machine Learning, Deep Learning, and AI
This capstone gathers the threads into one claim: Bayesian reasoning is the theory of learning under uncertainty, and it quietly underwrites much of machine learning. The loss is a likelihood. Regularization is a prior.
Đọc bài viết →Notation and Formula Reference
A compact reference for the symbols, rules, and formulas used throughout the book.
Đọc bài viết →Functions and the Idea of Calculus
Calculus studies how things change and how things accumulate. Before we can speak of change, we need the object that changes: the function. This chapter sets up functions and the handful of “essential” ones that appear e
Đọc bài viết →Limits and Continuity
The limit is the idea that makes calculus rigorous. “Instantaneous” rate of change and “infinitely thin” rectangles both hide a limit: a quantity we approach but may never reach. This chapter builds limits intuitively an
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